What is Machine Learning?
Machine Learning (ML) is a subset of artificial intelligence that focuses on building systems that learn from data, identify patterns, and make decisions with minimal human intervention.
Key concepts include:
- Supervised Learning – learning from labeled data.
- Unsupervised Learning – discovering hidden structures in unlabeled data.
- Reinforcement Learning – agents learning via rewards and penalties.
Common Algorithms
| Algorithm | Type | Typical Use‑Case |
|---|---|---|
| Linear Regression | Supervised | Predict continuous values |
| Logistic Regression | Supervised | Binary classification |
| K‑Means | Unsupervised | Clustering |
| Random Forest | Supervised | Ensemble classification/regression |
| Q‑Learning | Reinforcement | Game playing, robotics |
Interactive Quiz
Which algorithm is best suited for clustering?
Sample Code (Python)
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.ensemble import RandomForestClassifier
from sklearn.metrics import accuracy_score
# Dummy dataset
X = np.random.rand(200, 5)
y = (X[:, 0] + X[:, 1] > 1).astype(int)
X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=42)
model = RandomForestClassifier(n_estimators=100, random_state=42)
model.fit(X_train, y_train)
pred = model.predict(X_test)
print("Accuracy:", accuracy_score(y_test, pred))